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The use of virtual humans (i.e., avatars) holds the potential for interactive, automated interaction in domains such as remote communication, customer service, or public announcements. For signed language users, signing avatars could potentially provide accessible content by sharing information in the signer’s preferred or native language. As development of signing avatars has gained traction in recent years, many different methods of creating signing avatars have been developed, and the resulting avatars vary widely in their appearance, the naturalness of their movements, and their facial expressions--all of which may potentially impact users’ acceptance of the avatars. We designed a study to test the effects of these intrinsic properties of different signing avatars, while also examining the extent to which people’s own language experiences change their responses to signing avatars. We created video stimuli showing individual signs produced by 1) a live human signer (Human), 2) an avatar made using computer-synthesized animation (CS Avatar), and 3) an avatar made using high-fidelity motion capture (Mocap avatar). We surveyed 191 American Sign Language users, including Deaf (N = 83), Hard-of-Hearing (N = 34), and Hearing (N= 67) groups. Participants rated the three signers on multiple dimensions which were then combined to form ratings of Attitudes, Impressions, Comprehension, and Naturalness. Analyses demonstrated that the Mocap avatar was rated significantly more positively than the CS avatar on all primary variables. Correlations revealed that signers who acquire sign language later in life are more accepting of, and likely to have positive impressions of signing avatars. Finally, those who learned ASL earlier were more likely to give lower, more negative ratings to the CS avatar, but this association was not seen for the Mocap avatar or the Human signer. Together, these findings suggest that movement quality and appearance significantly impact users’ ratings of signing avatars, and show that signed language users with earlier age of ASL exposure are the most sensitive to movement quality issues seen in computer-generated avatars. We suggest that future efforts to develop signing avatars be considerate of retaining the fluid movement qualities which are integral to signed languages.

RNA secondary structure modeling is a challenging problem, and recent successes have raised the standards for accuracy, consistency, and tractability. Large increases in accuracy have been achieved by including data on reactivity toward chemical probes: Incorporation of 1M7 SHAPE reactivity data into an mfold-class algorithm results in median accuracies for base pair prediction that exceed 90%. However, a few RNA structures are modeled with significantly lower accuracy. Here, we show that incorporating differential reactivities from the NMIA and 1M6 reagents—which detect noncanonical and tertiary interactions—into prediction algorithms results in highly accurate secondary structure models for RNAs that were previously shown to be difficult to model. For these RNAs, 93% of accepted canonical base pairs were recovered in SHAPE-directed models. Discrepancies between accepted and modeled structures were small and appear to reflect genuine structural differences. Three-reagent SHAPE-directed modeling scales concisely to structurally complex RNAs to resolve the in-solution secondary structure analysis problem for many classes of RNA.

Retroviral virions initially assemble in an immature form that differs from that of the mature infectious particle. The RNA genomes in both immature and infectious particles are dimers, and interactions between the RNA dimer and the viral Gag protein ensure selective packaging into nascent immature virions. We used high-sensitivity selective 2′-hydroxyl acylation analyzed by primer extension (SHAPE) to obtain nucleotide-resolution structural information from scarce, femtomole quantities of Moloney murine leukemia virus (MuLV) RNA inside authentic virions and from viral RNA extracted from immature (protease-minus) virions. Our secondary structure model of the dimerization and packaging domain indicated that a stable intermolecular duplex known as PAL2, previously shown to be present in mature infectious MuLV particles, was sequestered in an alternate stem-loop structure inside immature virions. The intermediate state corresponded closely to a late-folding intermediate that we detected in time-resolved studies of the free MuLV RNA, suggesting that the immature RNA structure reflects trapping of the intermediate folding state by interactions in the immature virion. We propose models for the RNA-protein interactions that trap the RNA in the immature state and for the conformational rearrangement that occurs during maturation of virion particles.

RNA molecules function as the central conduit of information transfer in biology. To do this, they encode information both in their sequences and in their higher-order structures. Understanding the higher-order structure of RNA remains challenging. In this work we devise a simple, experimentally concise, and accurate approach for examining higher-order RNA structure by converting widely used massively parallel sequencing into an easily implemented single-molecule experiment for detecting through-space interactions and multiple conformations. We then use this experiment to analyze higher-order RNA structure, detect biologically important hidden states, and refine accurate three-dimensional structure models.

Chemical probing of RNA and DNA structure is a widely used and highly informative approach for examining nucleic acid structure and for evaluating interactions with protein and small-molecule ligands. Use of capillary electrophoresis to analyze chemical probing experiments yields hundreds of nucleotides of information per experiment and can be performed on automated instruments. Extraction of the information from capillary electrophoresis electropherograms is a computationally intensive multistep analytical process, and no current software provides rapid, automated, and accurate data analysis. To overcome this bottleneck, we developed a platform-independent, user-friendly software package, QuShape, that yields quantitatively accurate nucleotide reactivity information with minimal user supervision. QuShape incorporates newly developed algorithms for signal decay correction, alignment of time-varying signals within and across capillaries and relative to the RNA nucleotide sequence, and signal scaling across channels or experiments. An analysis-by-reference option enables multiple, related experiments to be fully analyzed in minutes. We illustrate the usefulness and robustness of QuShape by analysis of RNA SHAPE (selective 2′-hydroxyl acylation analyzed by primer extension) experiments.

RNA chaperones are ubiquitous, heterogeneous proteins essential for RNA structural biogenesis and function. We investigated the mechanism of chaperone-mediated RNA folding by following the time-resolved dimerization of the packaging domain of a retroviral RNA at nucleotide resolution. In the absence of the nucleocapsid (NC) chaperone, dimerization proceeded via multiple, slow-folding intermediates. In the presence of NC, dimerization occurred rapidly via a single structural intermediate. The RNA binding domain of hnRNP A1 protein (UP1), a structurally unrelated chaperone, also accelerated dimerization. Both chaperones interacted primarily with guanosine residues. Replacing guanosine with more weakly pairing inosine yielded an RNA that folded rapidly without a facilitating chaperone. These results show RNA chaperones can simplify RNA folding landscapes by weakening intramolecular interactions involving guanosine and explain many RNA chaperone activities.

We report the results of a first, collective, blind experiment in RNA three-dimensional (3D) structure prediction, encompassing three prediction puzzles. The goals are to assess the leading edge of RNA structure prediction techniques; compare existing methods and tools; and evaluate their relative strengths, weaknesses, and limitations in terms of sequence length and structural complexity. The results should give potential users insight into the suitability of available methods for different applications and facilitate efforts in the RNA structure prediction community in ongoing efforts to improve prediction tools. We also report the creation of an automated evaluation pipeline to facilitate the analysis of future RNA structure prediction exercises.

Nucleic acids are particularly amenable to structural characterization using chemical and enzymatic probes. Each individual structure mapping experiment reveals specific information about the structure and/or dynamics of the nucleic acid. Currently, there is no simple approach for making these data publically available in a standardized format. We therefore developed a standard for reporting the results of single nucleotide resolution nucleic acid structure mapping experiments, or SNRNASMs. We propose a schema for sharing nucleic acid chemical probing data that uses generic public servers for storing, retrieving, and searching the data. We have also developed a consistent nomenclature (ontology) within the Ontology of Biomedical Investigations (OBI), which provides unique identifiers (termed persistent URLs, or PURLs) for classifying the data. Links to standardized data sets shared using our proposed format along with a tutorial and links to templates can be found at http://snrnasm.bio.unc.edu.